{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.6"},"colab":{"name":"CNN-model.ipynb","provenance":[],"collapsed_sections":[],"machine_shape":"hm"},"accelerator":"TPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"khdqm8WAyPid","colab_type":"text"},"source":["# 为了防止Colab掉线，在控制台输入：\n","```\n","setInterval(()=>{\n","\tif(Array.from(document.getElementById(\"connect\").children[0].children[2].innerHTML).splice(3,4).toString() === '重,新,连,接'){\n","\t\tdocument.getElementById(\"connect\").children[0].children[2].click()\n","\t}\n","},1000)\n","```"]},{"cell_type":"code","metadata":{"id":"piAxFnqix9pW","colab_type":"code","outputId":"29871cc0-6e40-4ef1-db24-f5b2f2e04b20","executionInfo":{"status":"ok","timestamp":1578400748502,"user_tz":-480,"elapsed":37602,"user":{"displayName":"Matreshka Li","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mC7hvL8bh3rLf6yeBiFZHcnhpnRNiJae98fc6Rg=s64","userId":"06966273767401542548"}},"colab":{"base_uri":"https://localhost:8080/","height":124}},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"xozOpUIXkkKz","colab_type":"code","colab":{}},"source":["import tensorflow as tf\n","import matplotlib.pyplot as plt\n","import pandas as pd\n","import numpy as np\n","import matplotlib.image as imgplt\n","import os\n","import os.path\n","os.chdir('/content/gdrive/My Drive/Colab')"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xjOq-QU2a_CM","colab_type":"text"},"source":["## Sanity Check:检查label是否匹配"]},{"cell_type":"code","metadata":{"id":"ElKCcMdLkkK2","colab_type":"code","outputId":"36fac9dc-29ec-4ce5-e2da-2ed125b72246","executionInfo":{"status":"ok","timestamp":1578400781478,"user_tz":-480,"elapsed":1036,"user":{"displayName":"Matreshka Li","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mC7hvL8bh3rLf6yeBiFZHcnhpnRNiJae98fc6Rg=s64","userId":"06966273767401542548"}},"colab":{"base_uri":"https://localhost:8080/","height":202}},"source":["labels = pd.read_csv('origin_annotate.csv')\n","labels.head()\n","tmp_label = labels['synthesis_label'].to_list()\n","ground_label = tuple(eval(x) for x in tmp_label)\n","labels.head()"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>circle_origin</th>\n","      <th>big_arrow_end</th>\n","      <th>small_arrow_end</th>\n","      <th>synthesis_label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1000L-200_s-10.jpg</td>\n","      <td>(587, 819)</td>\n","      <td>(496, 1117)</td>\n","      <td>(411, 949)</td>\n","      <td>(587, 819, 496, 1117, 411, 949)</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1001L-200_s-10.jpg</td>\n","      <td>(587, 819)</td>\n","      <td>(500, 1119)</td>\n","      <td>(412, 950)</td>\n","      <td>(587, 819, 500, 1119, 412, 950)</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>100L-250_s-11.jpg</td>\n","      <td>(587, 819)</td>\n","      <td>(529, 1126)</td>\n","      <td>(419, 958)</td>\n","      <td>(587, 819, 529, 1126, 419, 958)</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>101L-250_s-11.jpg</td>\n","      <td>(587, 819)</td>\n","      <td>(533, 1126)</td>\n","      <td>(420, 960)</td>\n","      <td>(587, 819, 533, 1126, 420, 960)</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>102L-250_s-11.jpg</td>\n","      <td>(587, 819)</td>\n","      <td>(538, 1127)</td>\n","      <td>(421, 961)</td>\n","      <td>(587, 819, 538, 1127, 421, 961)</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           Unnamed: 0  ...                  synthesis_label\n","0  1000L-200_s-10.jpg  ...  (587, 819, 496, 1117, 411, 949)\n","1  1001L-200_s-10.jpg  ...  (587, 819, 500, 1119, 412, 950)\n","2   100L-250_s-11.jpg  ...  (587, 819, 529, 1126, 419, 958)\n","3   101L-250_s-11.jpg  ...  (587, 819, 533, 1126, 420, 960)\n","4   102L-250_s-11.jpg  ...  (587, 819, 538, 1127, 421, 961)\n","\n","[5 rows x 5 columns]"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"XWlNFtH_awW5","colab_type":"code","outputId":"edade006-0a05-42f9-beef-1c2f980cea47","executionInfo":{"status":"ok","timestamp":1578400782450,"user_tz":-480,"elapsed":1716,"user":{"displayName":"Matreshka Li","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mC7hvL8bh3rLf6yeBiFZHcnhpnRNiJae98fc6Rg=s64","userId":"06966273767401542548"}},"colab":{"base_uri":"https://localhost:8080/","height":259}},"source":["test_data_dir = r'/content/gdrive/My Drive/Colab/pic/labeled_data'\n","test_pic_name = '100L-250_s-11.jpg'\n","test_pic = os.path.join(test_data_dir,test_pic_name)\n","im = imgplt.imread(test_pic)\n","plt.imshow(im)\n","im.shape"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1088, 1920, 3)"]},"metadata":{"tags":[]},"execution_count":9},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAX0AAADhCAYAAAAkqmXdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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NQkZ8qseeEFLDMGAolwDX6vm6NHj/L88l+jEnFmzpzJ0qVLKSoqojjgpbY2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\nTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"qvizyhFNbGBc","colab_type":"text"},"source":["## 读取label文件，转换为数据集的目标数据"]},{"cell_type":"code","metadata":{"id":"0R7RUY4icRL8","colab_type":"code","colab":{}},"source":["working_dir = r'pic/labeled_data'\n","if(not os.path.exists(working_dir)):\n","    print('路径不存在')\n","pic = os.path.join(working_dir,'1001L-200_s-10.jpg')\n","im = imgplt.imread(pic)\n","WIDTH = im.shape[0]\n","LENGTH = im.shape[1]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"MXgrFCBPkkK5","colab_type":"code","outputId":"b9c0fc1d-981f-4d83-980f-d4317da499e6","executionInfo":{"status":"ok","timestamp":1578400783093,"user_tz":-480,"elapsed":718,"user":{"displayName":"Matreshka Li","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mC7hvL8bh3rLf6yeBiFZHcnhpnRNiJae98fc6Rg=s64","userId":"06966273767401542548"}},"colab":{"base_uri":"https://localhost:8080/","height":79}},"source":["flow_dataframe = pd.DataFrame({\n","    'name':labels['Unnamed: 0'],\n","    'circle_origin_v':tuple(x[0]/WIDTH for x in ground_label),\n","    'circle_origin_h':tuple(x[1]/LENGTH for x in ground_label),\n","    'big_arrow_end_v':tuple(x[2]/WIDTH for x in ground_label),\n","    'big_arrow_end_h':tuple(x[3]/LENGTH for x in ground_label),\n","    'small_arrow_end_v':tuple(x[4]/WIDTH for x in ground_label),\n","    'small_arrow_end_h':tuple(x[5]/LENGTH for x in ground_label),\n","})\n","flow_dataframe.head()\n","flow_dataframe.loc[flow_dataframe.loc[:,'name'] == test_pic_name].loc[:,:]"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>name</th>\n","      <th>circle_origin_v</th>\n","      <th>circle_origin_h</th>\n","      <th>big_arrow_end_v</th>\n","      <th>big_arrow_end_h</th>\n","      <th>small_arrow_end_v</th>\n","      <th>small_arrow_end_h</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2</th>\n","      <td>100L-250_s-11.jpg</td>\n","      <td>0.539522</td>\n","      <td>0.426563</td>\n","      <td>0.486213</td>\n","      <td>0.586458</td>\n","      <td>0.38511</td>\n","      <td>0.498958</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                name  circle_origin_v  ...  small_arrow_end_v  small_arrow_end_h\n","2  100L-250_s-11.jpg         0.539522  ...            0.38511           0.498958\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"vdF6Qs59rz3e","colab_type":"text"},"source":["## 读取数据集"]},{"cell_type":"code","metadata":{"id":"IVTge0chkkK8","colab_type":"code","outputId":"dae36b32-a977-4899-c087-bc2ff67dad19","executionInfo":{"status":"ok","timestamp":1578400784705,"user_tz":-480,"elapsed":976,"user":{"displayName":"Matreshka Li","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mC7hvL8bh3rLf6yeBiFZHcnhpnRNiJae98fc6Rg=s64","userId":"06966273767401542548"}},"colab":{"base_uri":"https://localhost:8080/","height":52}},"source":["from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","#归一化数据\n","train_datagen = ImageDataGenerator(rescale=1./255.,validation_split=0.1)\n","#直接从文件夹中读取图片数据\n","train_batch = 100\n","test_batch = 100\n","image_size = (500,500)\n","train_generator = train_datagen.flow_from_dataframe(\n","    dataframe = flow_dataframe,\n","    directory = working_dir, # 文件目录\n","    x_col = 'name',\n","    subset='training',\n","    y_col = np.array(['circle_origin_v','circle_origin_h','big_arrow_end_v','big_arrow_end_h','small_arrow_end_v','small_arrow_end_h']),\n","    target_size= image_size,  # 希望统一化的像素\n","    batch_size=train_batch,     #Batch大小\n","    shuffle = True,\n","    class_mode= 'raw',#'multi_output',\n","    seed = 42) \n","\n","validation_generator = train_datagen.flow_from_dataframe(\n","    dataframe = flow_dataframe,\n","    directory = working_dir, # 文件目录\n","    x_col = 'name',\n","    subset='validation',\n","    y_col = np.array(['circle_origin_v','circle_origin_h','big_arrow_end_v','big_arrow_end_h','small_arrow_end_v','small_arrow_end_h']),\n","    target_size=image_size,  # 希望统一化的像素\n","    batch_size=test_batch,     #Batch大小\n","    shuffle = True,\n","    class_mode= 'raw',#'multi_output',\n","    seed = 42) "],"execution_count":12,"outputs":[{"output_type":"stream","text":["Found 901 validated image filenames.\n","Found 100 validated image filenames.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"4kY91nnZr2wc","colab_type":"text"},"source":["## 定义模型"]},{"cell_type":"code","metadata":{"id":"Qw7OaunAWFZ0","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":124},"outputId":"4fc7b387-9297-49c2-da22-7ed4dd32e4f3","executionInfo":{"status":"ok","timestamp":1578400788703,"user_tz":-480,"elapsed":2951,"user":{"displayName":"Matreshka Li","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mC7hvL8bh3rLf6yeBiFZHcnhpnRNiJae98fc6Rg=s64","userId":"06966273767401542548"}}},"source":["TRANSFER_MODEL=tf.keras.applications.VGG19(input_shape=(image_size[0],image_size[1],3),include_top=False,weights='imagenet')\n","#TRANSFER_MODEL=tf.keras.applications.ResNet101V2(input_shape=(image_size[0],image_size[1],3),include_top=False,weights='imagenet')\n"],"execution_count":13,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n","Instructions for updating:\n","If using Keras pass *_constraint arguments to layers.\n","Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n","80142336/80134624 [==============================] - 1s 0us/step\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"9VjTnKBJWhSc","colab_type":"code","colab":{}},"source":["def create_model():\n","  model = tf.keras.models.Sequential()\n","  model.add(TRANSFER_MODEL)\n","  TRANSFER_MODEL.trainable=False\n","  model.add(tf.keras.layers.MaxPool2D((2,2)))\n","  model.add(tf.keras.layers.Flatten())\n","  model.add(tf.keras.layers.Dropout(0.25))\n","  model.add(tf.keras.layers.BatchNormalization())\n","  model.add(tf.keras.layers.Dense(32, activation='relu'))\n","  model.add(tf.keras.layers.Dropout(0.5))\n","  model.add(tf.keras.layers.BatchNormalization())\n","  model.add(tf.keras.layers.Dense(6, activation='sigmoid'))\n","  return model"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"TeUOvFSEkkLE","colab_type":"code","colab":{}},"source":["model = create_model()\n","model.compile(\n","  optimizer=tf.keras.optimizers.Adam(learning_rate=3e-1),\n","  loss='mean_squared_error',\n","  metrics=['MeanAbsoluteError'])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"pwfmA2oKNl7W","colab_type":"code","outputId":"80249b83-26b6-4914-9f28-41e8d81f7405","executionInfo":{"status":"ok","timestamp":1578400789051,"user_tz":-480,"elapsed":881,"user":{"displayName":"Matreshka Li","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mC7hvL8bh3rLf6yeBiFZHcnhpnRNiJae98fc6Rg=s64","userId":"06966273767401542548"}},"colab":{"base_uri":"https://localhost:8080/","height":468}},"source":["model.summary()"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","vgg19 (Model)                (None, 15, 15, 512)       20024384  \n","_________________________________________________________________\n","max_pooling2d (MaxPooling2D) (None, 7, 7, 512)         0         \n","_________________________________________________________________\n","flatten (Flatten)            (None, 25088)             0         \n","_________________________________________________________________\n","dropout (Dropout)            (None, 25088)             0         \n","_________________________________________________________________\n","batch_normalization (BatchNo (None, 25088)             100352    \n","_________________________________________________________________\n","dense (Dense)                (None, 32)                802848    \n","_________________________________________________________________\n","dropout_1 (Dropout)          (None, 32)                0         \n","_________________________________________________________________\n","batch_normalization_1 (Batch (None, 32)                128       \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 6)                 198       \n","=================================================================\n","Total params: 20,927,910\n","Trainable params: 853,286\n","Non-trainable params: 20,074,624\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"7y77ju1fmU_5","colab_type":"text"},"source":["#### Create paths to save model's weights"]},{"cell_type":"code","metadata":{"id":"uUxk0wbgmQQ8","colab_type":"code","colab":{}},"source":["file_path = r'model_checkpoints/'\n","model_save_name = r'weights.{epoch:02d}-{val_loss:.2f}.hdf5'\n","if(not os.path.exists(file_path)):\n","    print('路径不存在,已自动创建。')\n","    os.mkdir(file_path)\n","model_save_full_dir = os.path.join(file_path,model_save_name)\n","\n","save_model = tf.keras.callbacks.ModelCheckpoint(filepath=model_save_full_dir,save_best_only=True,save_weights_only=True)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"beKaE8kTmgb5","colab_type":"text"},"source":["#### Train the model"]},{"cell_type":"code","metadata":{"id":"DTSMiy7TkkLH","colab_type":"code","outputId":"cbe69218-94af-4f04-b5f4-85ea5febbfe8","executionInfo":{"status":"ok","timestamp":1578316244542,"user_tz":-480,"elapsed":213452,"user":{"displayName":"Matreshka Li","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mC7hvL8bh3rLf6yeBiFZHcnhpnRNiJae98fc6Rg=s64","userId":"06966273767401542548"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',factor=0.2,patience=3, min_lr=1e-9,verbose=1)\n","\n","history = model.fit_generator(\n","    train_generator,\n","    steps_per_epoch = train_generator.labels.shape[0]//train_batch,\n","    validation_data = validation_generator,\n","    validation_steps = validation_generator.labels.shape[0]//test_batch,\n","    validation_freq = 1,\n","    epochs = 30,\n","    verbose=2,\n","    callbacks=[reduce_lr,save_model])#tf.keras.callbacks.EarlyStopping()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Epoch 1/30\n","Epoch 1/30\n","9/9 - 167s - loss: 0.0250 - mean_absolute_error: 0.1168 - val_loss: 0.0352 - val_mean_absolute_error: 0.1606\n","Epoch 2/30\n","Epoch 1/30\n","9/9 - 161s - loss: 0.0147 - mean_absolute_error: 0.0906 - val_loss: 0.0127 - val_mean_absolute_error: 0.0803\n","Epoch 3/30\n","Epoch 1/30\n","9/9 - 177s - loss: 0.0094 - mean_absolute_error: 0.0714 - val_loss: 0.0165 - val_mean_absolute_error: 0.1024\n","Epoch 4/30\n","Epoch 1/30\n","9/9 - 163s - loss: 0.0106 - mean_absolute_error: 0.0732 - val_loss: 0.0314 - val_mean_absolute_error: 0.1307\n","Epoch 5/30\n","Epoch 1/30\n","9/9 - 159s - loss: 0.0115 - mean_absolute_error: 0.0751 - val_loss: 0.0105 - val_mean_absolute_error: 0.0788\n","Epoch 6/30\n","Epoch 1/30\n","9/9 - 164s - loss: 0.0100 - mean_absolute_error: 0.0692 - val_loss: 0.0143 - val_mean_absolute_error: 0.0987\n","Epoch 7/30\n","Epoch 1/30\n","9/9 - 160s - loss: 0.0096 - mean_absolute_error: 0.0693 - val_loss: 0.0119 - val_mean_absolute_error: 0.0930\n","Epoch 8/30\n","Epoch 1/30\n","\n","Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.06000000238418579.\n","9/9 - 160s - loss: 0.0100 - mean_absolute_error: 0.0668 - val_loss: 0.0156 - val_mean_absolute_error: 0.1105\n","Epoch 9/30\n","Epoch 1/30\n","9/9 - 160s - loss: 0.0085 - mean_absolute_error: 0.0609 - val_loss: 0.0136 - val_mean_absolute_error: 0.1017\n","Epoch 10/30\n","Epoch 1/30\n","9/9 - 159s - loss: 0.0087 - mean_absolute_error: 0.0601 - val_loss: 0.0158 - val_mean_absolute_error: 0.1043\n","Epoch 11/30\n","Epoch 1/30\n","\n","Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.012000000476837159.\n","9/9 - 161s - loss: 0.0072 - mean_absolute_error: 0.0607 - val_loss: 0.0131 - val_mean_absolute_error: 0.0981\n","Epoch 12/30\n","Epoch 1/30\n","9/9 - 160s - loss: 0.0069 - mean_absolute_error: 0.0569 - val_loss: 0.0117 - val_mean_absolute_error: 0.0934\n","Epoch 13/30\n","Epoch 1/30\n","9/9 - 159s - loss: 0.0078 - mean_absolute_error: 0.0574 - val_loss: 0.0122 - val_mean_absolute_error: 0.0964\n","Epoch 14/30\n","Epoch 1/30\n","\n","Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.002400000020861626.\n","9/9 - 177s - loss: 0.0062 - mean_absolute_error: 0.0544 - val_loss: 0.0133 - val_mean_absolute_error: 0.0967\n","Epoch 15/30\n","Epoch 1/30\n","9/9 - 144s - loss: 0.0080 - mean_absolute_error: 0.0536 - val_loss: 0.0133 - val_mean_absolute_error: 0.0963\n","Epoch 16/30\n","Epoch 1/30\n","9/9 - 160s - loss: 0.0072 - mean_absolute_error: 0.0540 - val_loss: 0.0130 - val_mean_absolute_error: 0.0957\n","Epoch 17/30\n","Epoch 1/30\n","\n","Epoch 00017: ReduceLROnPlateau reducing learning rate to 0.00048000002279877666.\n","9/9 - 176s - loss: 0.0061 - mean_absolute_error: 0.0530 - val_loss: 0.0131 - val_mean_absolute_error: 0.0962\n","Epoch 18/30\n","Epoch 1/30\n","9/9 - 162s - loss: 0.0063 - mean_absolute_error: 0.0526 - val_loss: 0.0128 - val_mean_absolute_error: 0.0954\n","Epoch 19/30\n","Epoch 1/30\n","9/9 - 160s - loss: 0.0067 - mean_absolute_error: 0.0532 - val_loss: 0.0126 - val_mean_absolute_error: 0.0943\n","Epoch 20/30\n","Epoch 1/30\n","\n","Epoch 00020: ReduceLROnPlateau reducing learning rate to 9.600000339560211e-05.\n","9/9 - 160s - loss: 0.0065 - mean_absolute_error: 0.0533 - val_loss: 0.0123 - val_mean_absolute_error: 0.0931\n","Epoch 21/30\n","Epoch 1/30\n","9/9 - 160s - loss: 0.0063 - mean_absolute_error: 0.0527 - val_loss: 0.0120 - val_mean_absolute_error: 0.0921\n","Epoch 22/30\n","Epoch 1/30\n","9/9 - 163s - loss: 0.0067 - mean_absolute_error: 0.0542 - val_loss: 0.0118 - val_mean_absolute_error: 0.0910\n","Epoch 23/30\n","Epoch 1/30\n","\n","Epoch 00023: ReduceLROnPlateau reducing learning rate to 1.920000067912042e-05.\n","9/9 - 160s - loss: 0.0068 - mean_absolute_error: 0.0530 - val_loss: 0.0115 - val_mean_absolute_error: 0.0900\n","Epoch 24/30\n","Epoch 1/30\n","9/9 - 177s - loss: 0.0061 - mean_absolute_error: 0.0530 - val_loss: 0.0114 - val_mean_absolute_error: 0.0892\n","Epoch 25/30\n","Epoch 1/30\n","9/9 - 142s - loss: 0.0075 - mean_absolute_error: 0.0529 - val_loss: 0.0111 - val_mean_absolute_error: 0.0881\n","Epoch 26/30\n","Epoch 1/30\n","\n","Epoch 00026: ReduceLROnPlateau reducing learning rate to 3.840000135824084e-06.\n","9/9 - 160s - loss: 0.0073 - mean_absolute_error: 0.0535 - val_loss: 0.0110 - val_mean_absolute_error: 0.0873\n","Epoch 27/30\n","Epoch 1/30\n","9/9 - 160s - loss: 0.0072 - mean_absolute_error: 0.0529 - val_loss: 0.0109 - val_mean_absolute_error: 0.0867\n","Epoch 28/30\n","Epoch 1/30\n","9/9 - 177s - loss: 0.0060 - mean_absolute_error: 0.0527 - val_loss: 0.0108 - val_mean_absolute_error: 0.0860\n","Epoch 29/30\n","Epoch 1/30\n","\n","Epoch 00029: ReduceLROnPlateau reducing learning rate to 7.680000635446049e-07.\n","9/9 - 144s - loss: 0.0080 - mean_absolute_error: 0.0529 - val_loss: 0.0106 - val_mean_absolute_error: 0.0852\n","Epoch 30/30\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"TEs_qEvgXv8X","colab_type":"code","outputId":"569cf11c-7020-4d8f-cbfe-63450d56110a","executionInfo":{"status":"ok","timestamp":1578311910000,"user_tz":-480,"elapsed":1350,"user":{"displayName":"Matreshka Li","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mC7hvL8bh3rLf6yeBiFZHcnhpnRNiJae98fc6Rg=s64","userId":"06966273767401542548"}},"colab":{"base_uri":"https://localhost:8080/","height":295}},"source":["plt.plot(history.history['mean_absolute_error'])\n","plt.plot(history.history['val_mean_absolute_error'])\n","plt.title('model MeanAbsoluteError')\n","plt.ylabel('MeanAbsoluteError')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'test'], loc='upper left')\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"guJ7ZakBlOTb","colab_type":"code","outputId":"f48a918c-6162-4173-c6f7-a0c324b6bb79","executionInfo":{"status":"ok","timestamp":1578293374014,"user_tz":-480,"elapsed":722,"user":{"displayName":"Matreshka Li","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mC7hvL8bh3rLf6yeBiFZHcnhpnRNiJae98fc6Rg=s64","userId":"06966273767401542548"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["weights_name = os.listdir(file_path)[-1]\n","\n","model.load_weights(filepath=os.path.join(file_path,weights_name))\n","print('已读取weights文件:\\t',weights_name)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["已读取weights文件:\t weights.65-0.01.hdf5\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"LvLWfbxvoLIF","colab_type":"text"},"source":["### Plot predictions"]},{"cell_type":"code","metadata":{"id":"luThejvDQE6s","colab_type":"code","colab":{}},"source":["def plot_dot(image,label,scale_factor,title=''):\n","  label_location = [None]*len(label)\n","  for cnt in range(len(label)):\n","    if(cnt%2 == 0):#index 0\\2\\4 对应纵坐标\n","      label_location[cnt] = int(label[cnt] * scale_factor[0])\n","    else:\n","      label_location[cnt] = int(label[cnt] * scale_factor[1])\n","  plt.imshow(image)\n","  plt.title(title)\n","  plt.scatter(label_location[1],label_location[0],c='m')# 画圆心\n","  plt.scatter(label_location[3],label_location[2],c='g')# 画大指针\n","  plt.scatter(label_location[5],label_location[4],c='b')# 画小指针"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"HtDsEVWZoPpF","colab_type":"text"},"source":["#### Plot validation data's predictions"]},{"cell_type":"code","metadata":{"id":"RFrMsXRCkkLJ","colab_type":"code","outputId":"639fc61c-7c4e-437e-903b-47349a91e195","executionInfo":{"status":"ok","timestamp":1578311928687,"user_tz":-480,"elapsed":6618,"user":{"displayName":"Matreshka Li","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mC7hvL8bh3rLf6yeBiFZHcnhpnRNiJae98fc6Rg=s64","userId":"06966273767401542548"}},"colab":{"base_uri":"https://localhost:8080/","height":542}},"source":["test_data_dir = r'/content/gdrive/My Drive/Colab/pic/labeled_data'\n","# Randomly pick N pictures for predicting\n","N = 3\n","np.random.seed()\n","random_index = np.random.randint(1,validation_generator.samples,size=(N,))\n","for val_sample in range(N):\n","  test_pic_name = validation_generator.filenames[random_index[val_sample]]\n","  test_pic = os.path.join(test_data_dir,test_pic_name)\n","  im = imgplt.imread(test_pic)\n","  tf_im = tf.keras.preprocessing.image.load_img(test_pic,color_mode='rgb',target_size=image_size)\n","  tf_im = tf.keras.preprocessing.image.img_to_array(tf_im,data_format=None,dtype=None)/255.\n","  plt.figure(figsize=(20,8))\n","  plt.subplot(N,2,2*(val_sample+1)-1)\n","  ground_truth_label = labels.loc[labels.loc[:,'Unnamed: 0'] == test_pic_name].loc[:,'synthesis_label']\n","  ground_truth_label = eval(list(ground_truth_label)[0])\n","  plot_dot(im,ground_truth_label,[1,1],'True label:'+test_pic_name)\n","  plt.subplot(N,2,2*(val_sample+1))\n","  output = model.predict(np.array([tf_im]))[0]\n","  plot_dot(im,output,[WIDTH,LENGTH],'CNN output prediction:'+test_pic_name)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA3wAAACvCAYAAACmVtw+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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MmERFpR/EffxKdFIMpzUJqlWzLPukKMGhpZdMql8txYP92ru4v8tHf/z32f+sl3C9db8Vi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\n","text/plain":["<Figure 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vvUg476EDyQgG8ySR5+fn867NPSdhgef1gZeftHrcsWkh1ZXmTy6pqo9gmj1CeXrJK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\nJi+Z5ChTzoLjvkt+1IfXtjFJG8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Y0udy2bJbNm088Hu/wPlKpFLm5uYw9/3wsPIw+91zEWNgpWP3c8wB8+X+f4Q/6SMai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\nRCSj1OATERERERHJqP8HYuNLOblc4hYAAAAASUVORK5CYII=\n","text/plain":["<Figure size 1440x576 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Q00Yg0ILVs9S","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}